Hierarchical Modeling Analyses Research Articles

Health Status Assessment of a Scraper Conveyor Mechanical System

A method leveraging cooperative game theory is proposed to determine the combined weights of the mechanical system of a scraper conveyor, with a particular mining face serving as the research object. Additionally, the immune principle is introduced to estimate the health status of the mechanical system. First, a hierarchical analysis model and an immune hierarchical analysis model are established for the mechanical system of the scraper conveyor. The analytic hierarchy process, fuzzy statistics method, and entropy weight method are employed to determine the individual weights of each component in the mechanical system. Subsequently, the combined weights of various components in the mechanical system of the scraper conveyor are calculated using the cooperative game method. Second, the health values and health status of each component are calculated based on the sequence vector of component states output by the immune principle and the comprehensive weight vector. Finally, the weight vector of the mechanical system of the scraper conveyor is obtained based on the analytic hierarchy process. The health status of the entire mechanical system is then evaluated using the health status data of each component as the initial data for the system layer. The research results demonstrate that the presented method effectively obtains the health status of the mechanical system of the scraper conveyor by considering the physical characteristics of each component. Moreover, it facilitates the effective formulation of maintenance and repair plans for various components of the system.

Insights into relationship of oxidative potential of particles in the atmosphere and entering the human respiratory system with particle size, composition and source: A case study in a coastal area in Northern China.

Oxidative potential (OP) of particulate matter (PM) is an important indicator of its health effects. However, the relationship between OP and its key influencing factors remains unclear. In this study, size-segregated PM samples were collected in Qingdao, China, with major components and OP of PM thoroughly examined. The PM composition and sources contributing to OP were determined by hierarchical cluster analysis and positive matrix factorization model, and deposition of size-segregated and source-specific PM in respiratory tract and its resulting OP were assessed by multiple path particle dosimetry model. Dithiothreitol (DTT) activity decreased with increase of particle size in winter, while larger particles (4.2-10.2 μm) also contributed significantly to OP in summer. WSOC strongly correlated with OP in different particle sizes, while water-soluble iron, zinc, lead, and manganese had strong correlations with DTT activity for fine particles, reflecting the co-effects of particle composition and size on OP. Coarse and fine particles were more likely to be deposited in head and pulmonary region, respectively, with traffic and industrial sources contributing significantly to the deposited OP, especially in deeper regions of respiratory tract. This study highlights that the combined effects of different factors on PM OP need to be considered in health-oriented pollution abatement.

Developing a sustainable development goals index for OECD countries: An effectiveness-based hierarchical data envelopment analysis

The Sustainable Development Goals (SDGs) consist of a set of multidimensional indicators comprising 17 goals and 169 targets from various perspectives. The use of a composite index is crucial for assessing the overall performance of each country in relation to the SDGs, given the extensive range of indicator measures involved. However, different weight schemes may lead to different results. Therefore, incorporating an objective weight is required to ensure the comparability and accuracy of the index. The main objective of this paper is to construct an SDG index for Organisation for Economic Co-operation and Development (OECD) countries using an effectiveness-based hierarchical data envelopment analysis (H-DEA) model and apply it to assess SDG effectiveness in OECD countries. The results of this study provide countries with more valuable insights based on the weights of the goals (indicators) that are endogenous to the dataset, assisting them in identifying priorities and strategies to improve future SDG performance.

Spatiotemporal patterns and association with climate for malaria elimination in Lao PDR: a hierarchical modelling analysis with two-step Bayesian model selection

BackgroundThe government of Lao PDR has increased efforts to control malaria transmission in order to reach its national elimination goal by 2030. Weather can influence malaria transmission dynamics and should be considered when assessing the impact of elimination interventions but this relationship has not been well characterized in Lao PDR. This study examined the space–time association between climate variables and Plasmodium falciparum and Plasmodium vivax malaria incidence from 2010 to 2022.MethodsSpatiotemporal Bayesian modelling was used to investigate the monthly relationship, and model selection criteria were used to evaluate the performance of the models and weather variable specifications. As the malaria control and elimination situation was spatially and temporally dynamic during the study period, the association was examined annually at the provincial level.ResultsMalaria incidence decreased from 2010 to 2022 and was concentrated in the southern regions for both P. falciparum and P. vivax. Rainfall and maximum humidity were identified as most strongly associated with malaria during the study period. Rainfall was associated with P. falciparum incidence in the north and central regions during 2010–2011, and with P. vivax incidence in the north and central regions during 2012–2015. Maximum humidity was persistently associated with P. falciparum and P. vivax incidence in the south.ConclusionsMalaria remains prevalent in Lao PDR, particularly in the south, and the relationship with weather varies between regions but was strongest for rainfall and maximum humidity for both species. During peak periods with suitable weather conditions, vector control activities and raising public health awareness on the proper usage of intervention measures, such as indoor residual spraying and personal protection, should be prioritized.

Research on Design Methods for Interactive Spaces in Schools for Children with Intellectual Disabilities Considering User Needs

To scientifically enhance user perception in decision-making for designing interactive spaces in schools for children with intellectual disabilities, we propose an innovative design model that integrates the Kano model, Analytic Hierarchy Process (AHP), and Axiomatic Design (AD) theories based on user needs. Initially, multi-method research was used to gather the original user requirements which were then refined through data cleaning to establish the initial user needs. The Kano model was then employed to categorize these initial user needs. AHP was then used to construct a hierarchical analysis model for the interactive spaces in schools for children with intellectual disabilities, creating a judgment matrix to accurately calculate demand weight values at each level. Subsequently, AHP was used to select the most important demand items. The independence axiom of AD theory was used to achieve a “Z”-shaped mapping between the functional requirements (FRs) and design parameters (DPs) for the interactive spaces in schools for children with intellectual disabilities. This mapping was analyzed using a matrix approach to assess the design rationality and optimize solutions, thereby transforming user needs into design parameters. Finally, the design parameters were used to create interactive spaces through computer-aided design, and the resulting design plans were evaluated. Experimental results indicate that this design scheme effectively translates subjective concepts into specific design parameters through a qualitative and quantitative approach. This significantly enhances the user needs of interactive spaces in schools for children with intellectual disabilities and provides a scientific basis for the architectural design of these schools.

Intelligent Evaluation of Power Tool Design Based on Deep Learning

In order to address the issue of intelligent development in power tool design, this paper proposes an intelligent evaluation method for power tool design based on deep learning technology. The paper begins by summarizing the evaluation indicators for power tool design and constructing a hierarchical analysis model. It then utilizes FAHP to calculate the weight of each design indicator. Subsequently, pictures are collected and manually annotated to create a dataset for power tools. Finally, the dataset is refined and trained using the ResNet algorithm to develop an intelligent evaluation model for power tools, enabling the intelligent assessment of each evaluation indicator. By comparing the results of manual evaluation with those obtained through intelligent evaluation, it demonstrates the accuracy of the intelligent evaluation model based on ResNet algorithm and validates the effectiveness of this experimental approach.

Bayesian Hierarchical Models for Subgroup Analysis.

In conventional subgroup analyses, subgroup treatment effects are estimated using data from each subgroup separately without considering data from other subgroups in the same study. The subgroup treatment effects estimated this way may be heterogenous with high variability due to small sample sizes in some subgroups and much different from the treatment effect in the overall population. A Bayesian hierarchical model (BHM) can be used to derive more precise, and less heterogenous estimates of subgroup treatment effects that are closer to the treatment effect in the overall population. BHM assumes exchangeability in treatment effect across subgroups after adjusting for effect modifiers and other relevant covariates. In this article, we will discuss the technical details for applying one-way and multi-way BHM using summary-level statistics, and patient-level data for subgroup analysis. Four case studies based on four new drug applications are used to illustrate the application of these models in subgroup analyses for continuous, dichotomous, time-to-event, and count endpoints.

Rapid assembly bolt connection design method for TBM disc cutter box based on AHP

With the TBM disc cutter box as the subject of investigation, this study addresses the intricate and labor-intensive nature of the assembly process in TBM disc cutter box welding assembly. In order to expedite the assembly of the cutter-head and disc cutter box, an alternative approach utilizing combined bolt connections is implemented in place of welding assembly. Therefore, this article proposes a TBM disc cutter combination bolt connection design method based on AHP. Initially, a hierarchical analysis model is constructed, taking into account the actual conditions of the TBM disc cutter box combined bolt connections. Subsequently, from the array of factors influencing bolted connection performance, five experimental variables were chosen, and an orthogonal experimental layout was devised to examine the stress dispersion in the TBM disc cutter box under conditions of bolted assembly, employing finite element analysis. By conducting extreme difference analysis, the study examined the effects of each experimental factor on the bolt connection performance, and determined the order of importance of each factor more objectively. Thus, the comprehensive weights of each factor were calculated, and the optimal solution of the bolt connection scheme was selected by synthesizing the effects of multiple factors.

A multiple criteria Bayesian hierarchical model for analyzing heterogeneous consumer preferences

We introduce a novel Bayesian hierarchical model for consumer preference analysis, addressing two significant challenges in this domain. First, it accommodates preference heterogeneity at both individual and segment levels. This enables actionable insights for targeting and pricing decisions while quantifying uncertainty. Second, it incorporates probabilistic value-based ranking to handle inconsistent and sparse preference data. This way, it mitigates the impact of cognitive biases and alleviates uncertainty in estimates. The proposed method performs robust inference of consumers’ preferences through hierarchical priors, allowing for flexible parameter learning and borrowing statistical strength from well-informed individuals. We demonstrate its practical usefulness by analyzing the real preferences of almost one hundred consumers considering mobile phone contracts. We also report the results of an extensive experimental study. The proposed method outperforms its counterpart, executing an independent estimation and the state-of-the-art approaches regarding predictive accuracy and preference similarity within identified customer groups. The performance improvements are more pronounced with larger sample sizes, smaller sets of items, and in contexts with reduced heterogeneity and increased consistency among consumers.

Measuring sustainability consciousness in Italy

Sustainability can be conceived as an ideal state in which society, environment and economy can thrive without harming each other in the present, future and global space. In this paper, we validate the Italian version of the sustainability consciousness questionnaire developed by a Swedish research group. The psychometric construct aims to measure individual sustainability consciousness through a combination of knowingness, attitudes and behaviour. The validation process is based on the hierarchical confirmatory factor analysis model and includes both the long and short forms of the questionnaire proposed in the original study. Specifically, we propose two alternative approaches to validate the short form, one based on the same subset of items as the original proposal and another based on a data-driven strategy that resulted in a different subset of items. Once validated on a national level, the sustainability consciousness questionnaire becomes a valuable tool for researchers and policymakers to assess the sustainability consciousness levels within a national or local population regarding this crucial issue, enabling the design of more precisely targeted policies.

Dielectric spectroscopy with machine learning integrated approach to analysis the influence of N:P ratio on algae growth modeling for forecasting

The task of monitoring algal biomass is a significant challenge in contemporary environmental conservation. A novel technique called dielectric spectroscopy with machine learning integration is an intelligent approach to address this challenge. This work optimized a machine learning supervised PCA, Hierarchical cluster analysis & linear regression model -strengthen the real-time dielectric spectroscopy approach to study the influence of N:P ratios on a model algae Desmodesmus sp. growth for forecasting. As a primary feature, different N:P concentrations (1:1, 2:1, 3:1, 1:2, and 1:3) were utilized to characterize the relationship between dielectric (S11(dB) & ε'') and conventional UV–vis spectroscopy properties (OD686 & Chl-a). The dielectric measurements of S11(dB) & ε'' are consistent with the OD686 & Chl-a value. Highest Desmodesmus sp. OD686 0.87, 0.76 recorded at N:P 3:1, 2:1 ratio respectively. The machine learning based PCA, Hierarchical cluster analysis and linear regression model were applied and found at 0–8 days incubation Desmodesmus sp. growth biomass negatively corelated with S11(dB) & ε“ and positively correlatedly with OD686 & Chl-a respectively, with R2 values > 0.95. While for the 0&8days measured ε”& S11(dB) were −6.169&-0.4765; −7.956 &-0.6775 respectively, the linear regression model was applied to predicted OD686 for 0&8 days was 0.299, 0.234; 0.817, 0.716 respectively, at the ratios N:P (3:1) and the RMES were 0.0047 & 0.0319 respectively. This analysis revealed that the machine learning based integrated approach has high accuracy. Thus, Dielectric spectroscopy and machine learning-integrated method could strengthen to the real-time monitoring of algal biomass or predicting to control the N:P ratios to protecting aquatic ecosystem.

Insect diversity estimation in polarimetric lidar.

Identifying flying insects is a significant challenge for biologists. Entomological lidar offers a unique solution, enabling rapid identification and classification in field settings. No other method can match its speed and efficiency in identifying insects in flight. This non-intrusive tool is invaluable for assessing insect biodiversity, informing conservation planning, and evaluating efforts to address declining insect populations. Although the species richness of co-existing insects can reach tens of thousands, current photonic sensors and lidars can differentiate roughly one hundred signal types. While the retrieved number of clusters correlate with Malaise trap diversity estimates, this taxonomic specificity, the number of discernible signal types is currently limited by instrumentation and algorithm sophistication. In this study, we report 32,533 observations of wild flying insects along a 500-meter transect. We report the benefits of lidar polarization bands for differentiating species and compare the performance of two unsupervised clustering algorithms, namely Hierarchical Cluster Analysis and Gaussian Mixture Model. Our analysis shows that polarimetric properties could be partially predicted even with unpolarized light, thus polarimetric lidar bands provide only a minor improvement in specificity. Finally, we use the physical properties of the clustered observations, such as wing beat frequency, daily activity patterns, and spatial distribution, to establish a lower bound for the number of species represented by the differentiated signal types.

Characteristics and Optimization of University English Translation from a Cross-cultural Perspective-Based on Functional Translation Theory

Abstract Drawing on the theoretical framework of functional translation, this study presents a multifaceted exploration of university English translation through functional, constructive, evolutionary, and cultural lenses. Positioned within a cross-cultural context, the paper categorizes university English translation into three primary attributes: the frontier nature of translation, linguistic and cultural divergences, and the professionalism required in translation activities. Utilizing Super Decision software, these attributes serve as input variables to construct a Network Hierarchical Analysis model. This model is further integrated with the Interpretive Structural Modeling (ISM) recursive structure to assess the influence of various factors on translation quality. The study employs a multidimensional approach to dissect the impact of cultural and professional variances on the quality of university English translation. The results reveal that 54 out of 65 cultural dimensions (83.08%) significantly affect translation quality. In the realm of professional characteristics, four out of six dimensions (66.67%) are found to have a considerable influence on translation quality. Based on these comprehensive findings, the paper proposes four strategic optimizations aimed at enhancing the quality of university English translation, thereby offering valuable insights and practical recommendations for the field.

The guiding significance of consumer visual perception on the packaging design of cultural and creative products

Abstract The packaging of cultural and creative products plays a crucial role in enhancing their competitiveness, with exquisite packaging significantly contributing to meeting the personalized needs of consumers and stimulating their desire to purchase. This paper examines the visual perception factors involved in the packaging design of cultural and creative products using the visual attention mechanism and categorizes the design factors according to demand using the Kano model. The hierarchical analysis method is used to determine the consumer demand weight of each design factor. The packaging solutions for cultural and creative products are finalized, and the TOPSIS method is employed to assess each solution. The hierarchical analysis model achieves consistency ratios of 0.076, 0.016, 0.003, and 0.004 for the target layer Z and the criterion layers M, O, and A, respectively, all of which are less than 0.1, thereby passing the consistency test. Among the three designed packaging schemes for cultural and creative products, scheme 1 has the largest S value, which is 0.899. Therefore, scheme 1 is the best design scheme. The existing sample scheme has a S value of 0.013, which is smaller than that of the other three design schemes. The integrated Kano-AHP-TOPSIS packaging design path’s design schemes for cultural and creative products are more likely to meet consumers’ needs, demonstrating the path’s practical feasibility and the importance of consumer visual perception in shaping packaging design for these products.

Matrix model-based Analysis of e-commerce development and urban entrepreneurial activity

Abstract China’s e-commerce market size, the most active and best-developed component of the digital economy, has become an essential driver of economic growth and urban entrepreneurial activity. This paper analyzes e-commerce development and urban entrepreneurial activity through BCG matrix. It establishes the framework of AHP-SWOT hierarchical analysis model combining with SWOT matrix model and AHP hierarchical analysis. In the development of e-commerce, the national e-commerce turnover in 2020 is 37.21 trillion yuan, with a year-on-year growth rate of 57.60% in 2014. Meanwhile, in 2020, the number of e-commerce employees reaches 60 million, which is 1.23 times of the number in 2014. Due to the rapid development of the economy and e-commerce, resulting in urban entrepreneurial activity is also gradually rising, the number of private enterprises in 2012 grew from 5254870 to 18921928 in 2020, an increase of up to 260%. 2020, the number of new market entities is 27,495,700, the country’s entrepreneurial activity is a year-on-year upward trend. Through the development of e-commerce and urban entrepreneurial activity, the quality of employment and entrepreneurship in society is promoted to a higher level.

Optimization of Food Distribution System Based on Dynamic Planning Model

Based on the data of per capita annual income, food production per hectare, and per capita consumption of consumers in different countries, this paper first establishes a dynamic planning model, starting from three indicators, namely, regional transportation time, purchasing capacity and actual demand, and using MATLAB, establishes a visual image to derive the changes of satisfaction, profitability, environment, and efficiency in the next ten years. In order to determine the priority of the four indicators, a hierarchical analysis model is established on the basis of the dynamic planning model to derive the calculated weights of each indicator. In order to verify the adaptability of the model, the model is applied to the developed country Britain and the developing country China, respectively, and the dynamic planning model is confirmed again according to the information returned by the visualization image, which illustrates the scalability of the model to the food distribution system in different countries and regions.

Exploring turn signal usage patterns in lane changes: A Bayesian hierarchical modelling analysis of realistic driving data

AbstractUsing turn signals to convey a driver's intention to change lanes provides a direct and unambiguous way of communicating with nearby drivers. Nonetheless, past research has indicated that drivers may not always use their turn signals before starting a lane change. In this study, realistic driving data are analyzed to investigate turn signal usage during lane changes on highways in and around Gothenburg, Sweden. Turn signal usage is examined and factors that influence it are identified by employing Bayesian hierarchical modelling. The study found that drivers used their turn signal before changing lanes in 60% of cases, after starting the lane change in 33% of cases, and did not use it at all in 7% of cases. The Bayesian hierarchical modelling results indicate that various factors, such as the speed and direction of lane changes and the presence of surrounding vehicles, influence the usage of turn signals. The study concludes that understanding the factors affecting turn signal usage is crucial for improving traffic safety in current and future mixed traffic with autonomous vehicles. The study discusses the implications of findings concerning increasing turn signal compliance through general policy‐making, improving existing in‐vehicle technologies and including turn signal usage in Pay‐As‐You‐Drive insurances.

Human ratings take time: A hierarchical facets model for the joint analysis of ratings and rating times.

Performance assessments increasingly utilize onscreen or internet-based technology to collect human ratings. One of the benefits of onscreen ratings is the automatic recording of rating times along with the ratings. Considering rating times as an additional data source can provide a more detailed picture of the rating process and improve the psychometric quality of the assessment outcomes. However, currently available models for analyzing performance assessments do not incorporate rating times. The present research aims to fill this gap and advance a joint modeling approach, the "hierarchical facets model for ratings and rating times" (HFM-RT). The model includes two examinee parameters (ability and time intensity) and three rater parameters (severity, centrality, and speed). The HFM-RT successfully recovered examinee and rater parameters in a simulation study and yielded superior reliability indices. A real-data analysis of English essay ratings collected in a high-stakes assessment context revealed that raters exhibited considerably different speed measures, spent more time on high-quality than low-quality essays, and tended to rate essays faster with increasing severity. However, due to the significant heterogeneity of examinees' writing proficiency, the improvement in the assessment's reliability using the HFM-RT was not salient in the real-data example. This discussion focuses on the advantages of accounting for rating times as a source of information in rating quality studies and highlights perspectives from the HFM-RT for future research on rater cognition.

Effect of Demographic Factors, Radiation Knowledge Level, Radiation Awareness on Radiation Benefit by Hierarchical Regression Analysis Model

Effect of Demographic Factors, Radiation Knowledge Level, Radiation Awareness on Radiation Benefit by Hierarchical Regression Analysis Model

Research on the cultivation path of craftsmanship in university education based on fuzzy hierarchical analysis

Abstract This paper firstly explores the evaluation method of the level of cultivation of craftsmanship in colleges and universities and decomposes the problem to be analyzed into each component element that affects its goal by using the fuzzy hierarchical evaluation method. The hierarchical structure analysis model is established, and the upper influence factors of each layer are used as the basic criteria to compare each influence factor of the program layer and the criterion layer with each other, and the fuzzy consistency matrix is constructed to determine the relative weights of each divided influence factor. Then, the current situation of the level of cultivation of craftsmanship in college education and the problems of the cultivation mechanism of craftsmanship in colleges and universities were analyzed, and suggestions for improvement were put forward. In terms of the existing problems, 70% of them think that the atmosphere of attaching importance to craftsmanship spirit is insufficient, 80% of them think that the teaching method of craftsmanship spirit is in a single way, and 86% of them think that the excavation of craftsmanship spirit is not deep. In terms of the recognition of craftsmanship spirit, “respecting teachers and respecting morality”, “being willing to devote”, “being rigorous and meticulous”, “inheriting tradition “ and “innovation and creation” are all around 60%, while “excellence”, “love for work”, and “endurance of loneliness” are all around 60%. The approval rate of the artisan spirit of “enduring loneliness” is between 70% and 75%. The research of this paper is of great significance to the cultivation of craftsmanship in universities.